In [1]:
import numpy as np
In [2]:
import pandas as pd 
import seaborn as sns
import matplotlib.pyplot as plt
In [3]:
import warnings
warnings.filterwarnings('ignore')
In [4]:
investdata = pd.read_csv('FDI data (1).csv')
Idata=investdata.copy()
In [5]:
Idata.head(10)
Out[5]:
Sector 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17
0 METALLURGICAL INDUSTRIES 22.69 14.14 36.61 8.11 200.38 149.13 169.94 1175.75 959.94 419.88 1098.14 1786.14 1466.23 567.63 359.34 456.31 1440.18
1 MINING 1.32 6.52 10.06 23.48 9.92 7.40 6.62 444.36 34.16 174.40 79.51 142.65 57.89 12.73 684.39 520.67 55.75
2 POWER 89.42 757.44 59.11 27.09 43.37 72.69 157.15 988.68 907.66 1271.79 1271.77 1652.38 535.68 1066.08 707.04 868.80 1112.98
3 NON-CONVENTIONAL ENERGY 0.00 0.00 1.70 4.14 1.27 1.35 2.44 58.82 125.88 622.52 214.40 452.17 1106.52 414.25 615.95 776.51 783.57
4 COAL PRODUCTION 0.00 0.00 0.00 0.04 0.00 9.14 1.30 14.08 0.22 0.00 0.00 0.00 0.00 2.96 0.00 0.00 0.00
5 PETROLEUM & NATURAL GAS 9.35 211.07 56.78 80.64 102.78 12.09 87.71 1405.04 349.29 265.53 556.43 2029.98 214.80 112.23 1079.02 103.02 180.40
6 BOILERS AND STEAM GENERATING PLANTS 0.00 0.00 0.00 0.04 0.54 0.00 3.31 1.51 0.00 3.96 0.63 31.79 20.05 0.17 1.33 77.91 53.91
7 PRIME MOVER (OTHER THAN ELECTRICAL GENERATORS) 0.00 0.00 0.00 0.00 2.66 0.74 25.57 40.53 74.88 39.50 166.44 313.75 184.60 212.78 230.70 159.13 286.88
8 ELECTRICAL EQUIPMENTS 79.76 65.76 34.71 73.20 97.40 39.50 76.85 653.74 417.35 728.27 153.90 566.39 195.87 134.31 574.83 444.88 2230.69
9 COMPUTER SOFTWARE & HARDWARE 228.39 419.39 314.24 368.32 527.90 1359.97 2613.33 1382.25 1543.34 871.86 779.81 796.35 485.96 1126.27 2296.04 5904.36 3651.71
In [6]:
Idata.columns
Out[6]:
Index(['Sector', '2000-01', '2001-02', '2002-03', '2003-04', '2004-05',
       '2005-06', '2006-07', '2007-08', '2008-09', '2009-10', '2010-11',
       '2011-12', '2012-13', '2013-14', '2014-15', '2015-16', '2016-17'],
      dtype='object')
In [7]:
Idata.shape
Out[7]:
(63, 18)
In [8]:
Idata.columns.isnull()
Out[8]:
array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False])
In [9]:
Fy1=list(Idata['2000-01'])
Fy1.sort()
print(Fy1)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 1.01, 1.05, 1.32, 1.42, 2.06, 2.41, 3.64, 4.03, 4.25, 5.42, 5.48, 8.07, 8.34, 9.35, 9.75, 11.49, 12.2, 13.2, 16.02, 17.52, 18.83, 20.23, 22.69, 24.33, 33.87, 35.94, 44.5, 45.75, 60.04, 67.72, 71.38, 79.76, 81.5, 89.42, 111.14, 177.69, 195.33, 228.39, 832.07]
In [10]:
Idata.plot.bar(x='Sector',y='2000-01',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2000-01')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2000-01 investment is high by Miscellaneous Industries and low by Rubber Goods and some sectors have zero investment.

In [11]:
Fy2=list(Idata['2001-02'])
Fy2.sort()
print(Fy2)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05, 0.11, 0.14, 0.18, 0.2, 0.36, 0.78, 0.94, 1.04, 2.33, 4.31, 4.54, 4.87, 5.07, 5.28, 6.52, 6.93, 8.37, 11.04, 12.47, 14.06, 14.14, 15.48, 16.7, 19.81, 32.04, 32.12, 42.35, 43.27, 46.39, 51.75, 61.4, 65.76, 66.22, 77.94, 87.23, 139.9, 187.95, 211.07, 219.39, 221.37, 235.76, 419.39, 757.44, 873.23]
In [12]:
Idata.plot.bar(x='Sector',y='2001-02',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2001-02')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2001-02 investment is high by Tele Communications and low by Timber Products and some sectors have zero investment.

In [13]:
Fy3=list(Idata['2002-03'])
Fy3.sort()
print(Fy3)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.04, 0.19, 0.21, 0.56, 0.6, 1.3, 1.31, 1.7, 2.03, 2.33, 3.8, 3.97, 5.22, 6.3, 7.36, 8.07, 10.06, 11.01, 13.48, 13.77, 14.17, 16.38, 16.42, 19.4, 21.08, 21.63, 25.7, 29.13, 29.32, 33.75, 34.71, 36.1, 36.5, 36.61, 36.88, 38.13, 40.07, 44.98, 45.07, 54.18, 56.78, 59.11, 128.12, 191.6, 218.76, 295.88, 296.34, 314.24, 419.96]
In [14]:
Idata.plot.bar(x='Sector',y='2002-03',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2002-03')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2002-03 investment is high by Automobile Industry and low by Leather goods and pickers and some sectors have zero investment.

In [15]:
Fy4=list(Idata['2003-04'])
Fy4.sort()
print(Fy4)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.02, 0.04, 0.04, 0.11, 0.19, 0.24, 0.3, 0.32, 0.43, 0.59, 0.94, 1.47, 1.69, 1.7, 1.96, 1.97, 2.95, 3.18, 4.14, 5.24, 6.37, 7.15, 7.55, 8.11, 9.34, 9.58, 10.41, 13.72, 20.24, 21.58, 21.95, 22.73, 23.48, 24.08, 27.09, 31.12, 46.2, 47.04, 47.54, 49.36, 54.51, 73.2, 80.64, 82.31, 86.49, 108.91, 109.22, 116.36, 119.09, 235.48, 271.15, 368.32]
In [16]:
Idata.plot.bar(x='Sector',y='2003-04',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2003-04')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2003-04 investment is high by Computer Software & Hardware and low by Earth-moving Machinery and some sectors have zero investment.

In [17]:
Fy5=list(Idata['2004-05'])
Fy5.sort()
print(Fy5)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.03, 0.05, 0.06, 0.07, 0.1, 0.16, 0.44, 0.47, 0.54, 0.89, 1.08, 1.18, 1.27, 1.97, 2.66, 2.7, 2.94, 3.83, 4.11, 5.35, 6.16, 8.36, 8.58, 8.89, 9.09, 9.85, 9.92, 10.75, 11.04, 12.83, 13.04, 13.46, 14.13, 14.22, 26.19, 26.79, 36.95, 37.01, 40.06, 43.04, 43.37, 43.98, 69.39, 88.1, 97.4, 102.78, 118.33, 121.83, 121.97, 139.0, 152.06, 200.38, 252.42, 293.36, 456.15, 527.9]
In [18]:
Idata.plot.bar(x='Sector',y='2004-05',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2004-05')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2004-05 investment is high by Computer Software & Hardware and low by Tea and Coffee(Processing & Warehousing coffee & Rubber) and some sectors have zero investment.

In [19]:
Fy6=list(Idata['2005-06'])
Fy6.sort()
print(Fy6)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.33, 0.38, 0.5, 0.59, 0.74, 0.81, 0.93, 1.11, 1.35, 1.43, 1.52, 3.0, 3.16, 4.24, 5.67, 7.4, 9.08, 9.14, 9.9, 10.27, 12.09, 12.31, 15.52, 22.62, 23.0, 25.54, 27.38, 28.93, 32.53, 34.09, 39.5, 40.91, 41.74, 42.8, 47.4, 50.87, 51.22, 53.63, 55.93, 71.78, 72.69, 87.42, 92.71, 94.33, 139.93, 149.13, 164.76, 169.83, 172.44, 228.71, 387.72, 452.08, 548.61, 617.98, 1359.97]
In [20]:
Idata.plot.bar(x='Sector',y='2005-06',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2005-06')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2004-05 investment is high by Computer Software & Hardware and low by Tea and Coffee(Processing & Warehousing coffee & Rubber) and some sectors have zero investment.

In [21]:
Fy6=list(Idata['2005-06'])
Fy6.sort()
print(Fy6)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.33, 0.38, 0.5, 0.59, 0.74, 0.81, 0.93, 1.11, 1.35, 1.43, 1.52, 3.0, 3.16, 4.24, 5.67, 7.4, 9.08, 9.14, 9.9, 10.27, 12.09, 12.31, 15.52, 22.62, 23.0, 25.54, 27.38, 28.93, 32.53, 34.09, 39.5, 40.91, 41.74, 42.8, 47.4, 50.87, 51.22, 53.63, 55.93, 71.78, 72.69, 87.42, 92.71, 94.33, 139.93, 149.13, 164.76, 169.83, 172.44, 228.71, 387.72, 452.08, 548.61, 617.98, 1359.97]
In [22]:
Idata.plot.bar(x='Sector',y='2005-06',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2005-06')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2005-06 investment is high by Computer Software & Hardware and low by Scientific Instruments and some sectors have zero investment.

In [23]:
Fy7=list(Idata['2006-07'])
Fy7.sort()
print(Fy7)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04, 0.07, 0.99, 1.3, 1.43, 2.44, 2.81, 3.31, 5.01, 5.08, 5.42, 6.04, 6.2, 6.62, 8.26, 9.85, 12.53, 13.43, 16.22, 18.75, 20.04, 25.19, 25.57, 25.82, 25.96, 27.58, 37.28, 38.14, 41.81, 42.14, 61.97, 62.29, 64.06, 72.52, 75.7, 76.85, 84.44, 87.71, 102.0, 114.65, 114.68, 122.23, 126.9, 138.85, 157.15, 169.94, 195.66, 224.2, 242.47, 260.72, 304.87, 476.51, 1392.95, 2613.33, 4713.78]
In [24]:
Idata.plot.bar(x='Sector',y='2006-07',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2006-07')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2006-07 investment is high by Services Sector(Eg.Banking&Insurance.etc) and low by coir and some sectors have zero investment.

In [25]:
Fy8=list(Idata['2007-08'])
Fy8.sort()
print(Fy8)

[0.0, 0.0, 0.01, 0.39, 1.27, 1.27, 1.51, 1.53, 1.95, 2.23, 2.28, 5.51, 6.38, 6.72, 7.46, 9.83, 10.07, 11.04, 12.41, 13.17, 14.08, 15.12, 16.89, 18.94, 31.24, 35.54, 40.53, 43.59, 54.86, 56.87, 58.13, 58.82, 59.15, 67.94, 70.17, 99.08, 115.11, 119.57, 128.36, 136.03, 182.92, 185.4, 211.15, 233.32, 233.7, 241.76, 270.05, 288.49, 340.35, 345.02, 421.47, 444.36, 528.42, 653.74, 656.1, 918.18, 988.68, 1175.75, 1260.7, 1382.25, 1405.04, 3887.33, 6986.17]
In [26]:
Idata.plot.bar(x='Sector',y='2007-08',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2007-08')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2007-08 investment is high by Services Sector(Eg.banking&Finance.etc) and low by Coir and some sectors have zero investment.

In [27]:
Fy9=list(Idata['2008-09'])
Fy9.sort()
print(Fy9)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.09, 0.22, 0.83, 1.05, 1.17, 2.27, 3.32, 5.01, 5.35, 5.57, 11.27, 12.63, 17.48, 18.01, 22.03, 23.16, 31.61, 34.16, 37.08, 42.88, 45.66, 50.21, 61.37, 74.88, 75.42, 83.5, 84.88, 102.71, 110.54, 125.88, 133.75, 142.31, 144.7, 147.51, 157.52, 172.7, 198.43, 214.52, 239.71, 256.59, 272.51, 349.29, 417.35, 463.92, 493.15, 643.64, 651.22, 724.8, 735.04, 907.66, 959.94, 1150.03, 1543.34, 1549.7, 2548.63, 4246.76, 4657.51, 6183.49]
In [28]:
Idata.plot.bar(x='Sector',y='2008-09',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2008-09')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2008-09 investment is high by Services Sector(Eg.Banking&Finance.etc) and low by Retail Trading and some sectors have zero investment.

In [29]:
Fy10=list(Idata['2009-10'])
Fy10.sort()
print(Fy10)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.25, 0.27, 1.88, 2.83, 3.96, 4.02, 5.06, 6.54, 7.21, 7.61, 8.15, 8.2, 13.73, 15.62, 16.42, 23.71, 24.12, 24.58, 31.08, 33.8, 34.43, 39.5, 52.14, 63.35, 65.41, 69.74, 70.51, 78.98, 112.02, 133.83, 135.57, 149.59, 150.27, 167.35, 174.4, 213.08, 265.53, 278.89, 284.85, 324.56, 341.31, 341.88, 365.94, 419.88, 490.83, 622.52, 728.27, 737.95, 753.02, 871.86, 1147.56, 1222.22, 1236.29, 1271.79, 2539.26, 4174.53, 5466.13]
In [30]:
Idata.plot.bar(x='Sector',y='2009-10',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2009-10')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2009-10 investment is high by Construction Development and low by Sugar and some sectors have zero investment.

In [31]:
Fy11=list(Idata['2010-11'])
Fy11.sort()
print(Fy11)

[0.0, 0.0, 0.0, 0.01, 0.1, 0.17, 0.49, 0.63, 0.81, 1.58, 1.77, 2.49, 3.12, 5.37, 6.53, 7.6, 9.26, 10.92, 11.63, 12.0, 17.21, 18.18, 19.59, 25.12, 25.48, 26.27, 32.22, 36.63, 37.94, 43.9, 57.71, 58.07, 59.72, 70.66, 79.51, 102.9, 108.67, 129.65, 136.6, 153.9, 166.44, 188.67, 209.38, 214.4, 256.0, 274.84, 300.51, 308.05, 412.11, 467.92, 498.04, 556.43, 637.68, 675.07, 779.81, 1098.14, 1271.77, 1299.41, 1475.97, 1663.03, 1664.5, 2354.4, 3296.09]
In [32]:
Idata.plot.bar(x='Sector',y='2010-11',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2010-11')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2010-11 investment is high by services Sector(Eg.Banking&Finance) low by Coir and some sectors have zero investment.

In [33]:
Fy12=list(Idata['2011-12'])
Fy12.sort()
print(Fy12)

[0.0, 0.0, 0.0, 0.0, 0.55, 0.58, 2.77, 3.66, 3.99, 4.44, 5.32, 5.84, 7.08, 8.3, 9.87, 16.4, 29.04, 29.6, 31.22, 31.7, 31.79, 32.22, 32.6, 36.3, 42.27, 47.39, 49.02, 65.02, 69.7, 105.62, 127.87, 129.36, 141.61, 142.65, 164.19, 170.21, 187.37, 194.41, 222.08, 267.9, 289.89, 310.43, 313.75, 386.28, 407.35, 452.17, 566.39, 620.66, 675.96, 731.55, 796.35, 813.38, 922.99, 992.86, 1295.34, 1652.38, 1786.14, 1997.24, 2029.98, 3140.78, 3232.28, 4040.71, 5215.98]
In [34]:
Idata.plot.bar(x='Sector',y='2011-12',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2011-12')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2011-12 investment is high by Services Sector(Eg.banking&Finance.etc) and low by Coir and some sectors have zero investment.

In [35]:
Fy13=list(Idata['2012-13'])
Fy13.sort()
print(Fy13)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.15, 0.27, 0.41, 0.58, 4.33, 5.09, 5.1, 6.71, 12.26, 12.38, 14.34, 15.89, 20.05, 22.31, 24.33, 29.17, 29.85, 38.24, 42.55, 46.7, 52.61, 57.89, 64.62, 73.28, 83.02, 89.45, 95.41, 101.39, 103.89, 107.21, 108.39, 142.32, 160.07, 161.47, 172.2, 184.6, 195.87, 209.16, 214.8, 229.49, 256.86, 283.89, 292.16, 303.87, 401.46, 404.04, 485.96, 503.83, 535.68, 642.18, 717.8, 1106.52, 1123.46, 1332.49, 1466.23, 1537.28, 3259.05, 4832.98]
In [36]:
Idata.plot.bar(x='Sector',y='2012-13',legend=None,figsize=(15,10),rot=0)
plt.ylabel('20012-13')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2012-13 investment is high by Services Sector(Eg.banking&Finance.etc) and low by Coir and some sectors have zero investment.

In [37]:
Fy14=list(Idata['2013-14'])
Fy14.sort()
print(Fy14)

[0.0, 0.0, 0.0, 0.17, 0.31, 0.53, 0.54, 0.82, 0.97, 2.96, 3.08, 5.86, 6.14, 6.16, 11.3, 12.73, 20.49, 20.65, 21.13, 21.55, 26.86, 34.44, 42.56, 43.09, 45.08, 45.95, 48.78, 64.52, 91.01, 108.44, 112.23, 113.78, 132.58, 134.31, 150.09, 173.48, 198.86, 212.78, 236.93, 254.01, 262.09, 285.85, 288.13, 370.54, 414.25, 428.52, 468.74, 477.38, 485.37, 486.38, 567.63, 684.58, 786.76, 814.58, 1066.08, 1126.27, 1226.05, 1279.34, 1306.95, 1343.39, 1517.28, 2225.1, 3982.89]
In [38]:
Idata.plot.bar(x='Sector',y='2013-14',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2013-14')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2013-14 investment is high by Food Processing Industries and low by Boilers & Steam Generating Plants and some sectors have zero investment.

In [39]:
Fy15=list(Idata['2014-15'])
Fy15.sort()
print(Fy15)

[0.0, 0.0, 0.08, 0.75, 0.85, 1.33, 1.36, 1.43, 1.9, 8.97, 21.44, 24.06, 27.77, 30.11, 32.34, 33.39, 34.21, 35.29, 41.82, 54.89, 59.95, 72.35, 72.58, 74.56, 78.86, 96.84, 116.21, 129.73, 145.93, 148.34, 168.72, 177.22, 186.69, 197.42, 208.99, 225.32, 225.38, 230.7, 254.96, 280.18, 284.51, 333.22, 359.34, 458.13, 515.86, 567.85, 574.83, 615.95, 684.39, 707.04, 716.79, 762.76, 765.88, 769.14, 777.01, 870.25, 1079.02, 1497.74, 2296.04, 2725.64, 2727.96, 2894.94, 4443.26]
In [40]:
Idata.plot.bar(x='Sector',y='2014-15',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2014-15')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2014-15 investment is high by Services Sector(Eg.banking&Finance.etc) and low by Defence Industries and some sectors have zero investment.

In [41]:
Fy16=list(Idata['2015-16'])
Fy16.sort()
print(Fy16)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.82, 1.12, 3.32, 6.36, 7.42, 16.44, 17.13, 19.69, 20.93, 25.78, 34.22, 36.68, 51.21, 53.17, 58.54, 73.99, 77.91, 84.65, 85.21, 97.66, 103.02, 105.85, 112.55, 122.81, 126.38, 159.13, 173.26, 193.26, 202.36, 208.39, 230.13, 230.78, 262.24, 274.57, 296.15, 361.25, 429.3, 444.88, 456.31, 505.88, 517.47, 520.67, 568.26, 668.77, 742.35, 754.26, 776.51, 868.8, 1009.34, 1324.4, 1332.69, 1469.95, 2526.82, 3845.32, 4510.71, 5904.36, 6889.46]
In [42]:
Idata.plot.bar(x='Sector',y='2015-16',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2015-16')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2015-16 investment is high by Services Sector(Eg.banking&Finance.etc) and low by Defence Industries and some sectors have zero investment.

In [43]:
Fy17=list(Idata['2016-17'])
Fy17.sort()
print(Fy17)

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.89, 1.6, 2.3, 7.44, 10.23, 10.7, 15.19, 15.4, 15.92, 23.89, 51.69, 52.23, 53.17, 53.91, 55.75, 76.43, 76.66, 83.4, 83.97, 87.57, 90.6, 92.6, 105.14, 108.45, 110.86, 123.92, 160.12, 180.4, 197.61, 245.24, 261.14, 262.76, 286.88, 296.4, 329.3, 450.94, 479.71, 618.95, 727.22, 735.06, 747.38, 783.57, 857.39, 916.13, 1112.98, 1392.8, 1440.18, 1516.68, 1609.32, 1860.73, 2130.1, 2230.69, 2338.4, 3651.71, 5563.69, 8684.07]
In [44]:
Idata.plot.bar(x='Sector',y='2016-17',legend=None,figsize=(15,10),rot=0)
plt.ylabel('2016-17')
plt.xticks(rotation=90)
plt.locator_params(nbins=90)

From the above bar plot we can conclude that in the financial year 2016-17 investment is high by Services Sector(Eg.banking&insurance.etc) and low by Industrial Instruments and some sectors have zero investment.

From the above all sector-wise vs financial year bar graphs and the Teableau visualizations we can conclude that Services Sector(Eg.Banking&Finance etc.) is the high investment sector in maximum number of financial years followed by Computers Software & Hardware sector and Coir is the low investment sector in maximum number of financial years and in every financial year some sectors have zero investment.

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